U.S. patent application number 13/605853 was filed with the patent office on 2014-03-06 for context-based position determination.
This patent application is currently assigned to QUALCOMM Incorporated. The applicant listed for this patent is Saumitra Mohan Das, Ayman Fawzy Naguib. Invention is credited to Saumitra Mohan Das, Ayman Fawzy Naguib.
Application Number | 20140064112 13/605853 |
Document ID | / |
Family ID | 47178304 |
Filed Date | 2014-03-06 |
United States Patent
Application |
20140064112 |
Kind Code |
A1 |
Das; Saumitra Mohan ; et
al. |
March 6, 2014 |
CONTEXT-BASED POSITION DETERMINATION
Abstract
Disclosed is a method for position determination, including
obtaining measurements of at least one characteristic of one or
more wireless signals acquired at a mobile station, obtaining a
classification of a context of a user co-located with the mobile
station, and affecting application of a representation of the
signal environment to the measurements for obtaining a position fix
based, at least in part, on the classification of the context.
Inventors: |
Das; Saumitra Mohan; (San
Jose, CA) ; Naguib; Ayman Fawzy; (Cupertino,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Das; Saumitra Mohan
Naguib; Ayman Fawzy |
San Jose
Cupertino |
CA
CA |
US
US |
|
|
Assignee: |
QUALCOMM Incorporated
San Diego
CA
|
Family ID: |
47178304 |
Appl. No.: |
13/605853 |
Filed: |
September 6, 2012 |
Current U.S.
Class: |
370/252 |
Current CPC
Class: |
H04W 64/006 20130101;
G01S 5/0257 20130101; G01S 19/48 20130101; H04W 4/33 20180201; H04W
4/027 20130101; G01S 5/0252 20130101; H04W 4/024 20180201; G01S
5/0263 20130101 |
Class at
Publication: |
370/252 |
International
Class: |
H04W 24/00 20090101
H04W024/00 |
Claims
1. A method comprising: obtaining measurements of at least one
characteristic of one or more wireless signals acquired at a mobile
station while located in a signal environment; obtaining a
classification of a context of a user co-located with said mobile
station; and affecting application of a representation of said
signal environment to said measurements for obtaining a position
fix based, at least in part, on said classification of said
context.
2. The method of claim 1, wherein said representation of said
signal environment comprises a wireless signal fingerprint.
3. The method of claim 1, wherein said representation of said
signal environment comprises a heatmap.
4. The method of claim 3, wherein said at least one characteristic
comprises a received signal strength indicator (RSSI), and wherein
said affecting said application of said heatmap further comprises:
changing an expected RSSI value for a location, said changing
based, at least in part, on the classification; and attempting to
match at least one of said measurements to the changed expected
RSSI value.
5. The method of claim 4, wherein said changing said expected RSSI
value comprises adding/subtracting a quantity of RSSI to/from said
expected RSSI value.
6. The method of claim 3, wherein said heatmap comprises expected
RSSI values associated with particular access points.
7. The method of claim 3, wherein said heatmap comprises values for
expected received signal strength indicator (RSSI), RSSI variances,
round-trip time (RTT), and RTT variances associated with particular
MAC IDs associated with access points.
8. The method of claim 1, wherein said obtaining said
classification of said context of said user further comprises:
receiving sensor measurements from one or more sensors of said
mobile station; and comparing said sensor measurements to values in
a lookup table stored in said mobile station to classify said
context of said user.
9. The method of claim 8, wherein said one or more sensors comprise
an inertial sensor, a proximity sensor, a temperature sensor, a
compass, a gravitometer, or an audio sensor.
10. The method of claim 8, wherein said obtaining said
classification of said context of said user is based, at least in
part, on elapsed time of a state of said user.
11. The method of claim 10, wherein said state of said user
comprises sitting, standing, or moving.
12. The method of claim 8, wherein said values in said lookup table
are based, at least in part, on empirical data corresponding to a
plurality of context classifications.
13. The method of claim 1, wherein said representation is based, at
least in part, on map information, and wherein said affecting said
application of said representation is further based, at least in
part, on additional map information corresponding to a particular
context classification of said user.
14. The method of claim 13, wherein said particular context
classification of said user comprises a sitting state and said
additional map information comprises cubicle partition
locations.
15. The method of claim 14, wherein said particular context
classification of said user further comprises a location of said
sitting state.
16. The method of claim 1, further comprising: changing a frequency
of measuring said at least one characteristic of said one or more
wireless signals based, at least in part, on said context.
17. The method of claim 1, further comprising: changing an
operation of a particle filter based, at least in part, on the
classification by changing a quantity of new particles or a
velocity of particle propagation.
18. The method of claim 1, wherein said affecting application of
said representation of said signal environment is further based, at
least in part, on one or more behaviors of said user.
19. The method of claim 1, wherein said obtaining said
classification of said context and said affecting application of
said representation of said signal environment is performed
on-the-fly.
20. The method of claim 1, wherein said method is performed at
least partially at said mobile station.
21. The method of claim 1, wherein said method is performed at
least partially at a land-based server.
22. An apparatus comprising: means for obtaining measurements of at
least one characteristic of one or more wireless signals acquired
at a mobile station while located in a signal environment; means
for obtaining a classification of a context of a user co-located
with said mobile station; and means for affecting application of a
representation of said signal environment to said measurements for
obtaining a position fix based, at least in part, on said
classification of said context.
23. An apparatus comprising: a transceiver to obtain measurements
of at least one characteristic of one or more wireless signals
acquired at a mobile station while located in a signal environment;
and one or more processing units to: obtain a classification of a
context of a user co-located with said mobile station; and affect
application of a representation of said signal environment to said
measurements for obtaining a position fix based, at least in part,
on said classification of said context.
24. The apparatus of claim 23, wherein said representation of said
signal environment comprises a wireless signal fingerprint.
25. The apparatus of claim 23, wherein said representation of said
signal environment comprises a heatmap.
26. The apparatus of claim 25, wherein said at least one
characteristic comprises a received signal strength indicator
(RSSI), and wherein said one or more processing units are
configured to affect application of said heatmap by: changing an
expected RSSI value for a location, said changing based, at least
in part, on the classification; and attempting to match at least
one of said measurements to the changed expected RSSI value.
27. The apparatus of claim 26, wherein said one or more processing
units are configured to change said expected RSSI value by
adding/subtracting a quantity of RSSI to/from said expected RSSI
value.
28. The apparatus of claim 25, wherein said heatmap comprises
expected RSSI values associated with particular access points.
29. The apparatus of claim 25, wherein said heatmap comprises
values for expected received signal strength indicator (RSSI), RSSI
variances, round-trip time (RTT), and RTT variances associated with
particular MAC IDs associated with access points.
30. The apparatus of claim 23, wherein said one or more processing
units are configured to obtain said classification of said context
of said user by: receiving sensor measurements from one or more
sensors of said mobile station; and comparing said sensor
measurements to values in a lookup table stored in said mobile
station to classify said context of said user.
31. The apparatus of claim 30, wherein said one or more sensors
comprise an inertial sensor, a proximity sensor, a temperature
sensor, a compass, a gravitometer, or an audio sensor.
32. The apparatus of claim 30, wherein said one or more processing
units are configured to obtain said classification of said context
of said user based, at least in part, on elapsed time of a state of
said user.
33. The apparatus of claim 32, wherein said state of said user
comprises sitting, standing, or moving.
34. The apparatus of claim 30, wherein said values in said lookup
table are based, at least in part, on empirical data corresponding
to a plurality of context classifications.
35. The apparatus of claim 23, wherein said representation is
based, at least in part, on map information, and wherein said one
or more processing units are configured to affect said application
of said representation based, at least in part, on additional map
information corresponding to a particular context classification of
said user.
36. The apparatus of claim 35, wherein said particular context
classification of said user comprises a sitting state and said
additional map information comprises cubicle partition
locations.
37. The apparatus of claim 36, wherein said particular context
classification of said user further comprises a location of said
sitting state.
38. The apparatus of claim 23, wherein said one or more processing
units are configured to: change a frequency of measuring said at
least one characteristic of said one or more wireless signals
based, at least in part, on said context.
39. The apparatus of claim 23, wherein said one or more processing
units are configured to: change an operation of a particle filter
based, at least in part, on the classification by changing a
quantity of new particles or a velocity of particle
propagation.
40. The apparatus of claim 23, wherein said one or more processing
units are configured to affect application of said representation
of said signal environment based, at least in part, on one or more
behaviors of said user.
41. The apparatus of claim 23, wherein said one or more processing
units are configured to obtain said classification of said context
and affect application of said representation of said signal
environment on-the-fly.
42. A non-transitory storage medium comprising machine-readable
instructions stored thereon that are executable by a special
purpose computing device to: obtain measurements of at least one
characteristic of one or more wireless signals acquired at a mobile
station while located in a signal environment; obtain a
classification of a context of a user co-located with said mobile
station; and affect application of a representation of said signal
environment to said measurements for obtaining a position fix
based, at least in part, on said classification of said
context.
43. A method comprising: obtaining measurements of at least one
characteristic of one or more wireless signals acquired at a mobile
station; determining a representation of a signal environment in
which said one or more wireless signals were acquired based, at
least in part, on a detected context of said mobile station; and
estimating a location of the mobile station based, at least in
part, on a match of the obtained measurements with the determined
representation.
44. The method of claim 43, further comprising: maintaining a
database of expected signal characteristics associated with
locations in an area, wherein the determining comprises modifying
said database of expected signal characteristics based, at least in
part, on said detected context of said mobile station, and wherein
the estimating comprises estimating the location of the mobile
station based, at least in part, on a match of the obtained
measurements with the modified expected signal characteristics.
45. The method of claim 44, wherein said representation of said
signal environment is based, at least in part, on a map of said
area, and wherein said modifying said database is further based, at
least in part, on additional information for said map, said
additional information corresponding to a particular context
classification of said mobile station.
46. The method of claim 45, wherein said particular context
classification of said mobile station comprises a sitting state and
said additional information comprises cubicle partition locations
in said area, and wherein said particular context classification of
said mobile station further comprises a location of said sitting
state.
47. The method of claim 43, wherein said representation of said
signal environment comprises a heatmap or a wireless signal
fingerprint.
48. The method of claim 43, wherein said determining comprises:
selecting one representation of said signal environment among a
plurality of stored representations of said signal environment
based, at least in part, on said detected context.
49. The method of claim 43, wherein said determining comprises:
calculating said representation of said signal environment based,
at least in part, on said detected context.
50. The method of claim 43, wherein said representation of said
signal environment comprises a received signal strength indicator
(RSSI).
51. The method of claim 43, wherein said detected context of said
mobile station comprises a position-and-motion state of said mobile
station.
52. The method of claim 43, wherein said detected context of said
mobile station is based, at least in part, on sensor measurements
from one or more sensors of said mobile station and on values in a
lookup table stored in said mobile station.
53. The method of claim 43, wherein said detected context of said
mobile station is further based, at least in part, on elapsed time
of a state of said mobile station.
54. The method of claim 43, further comprising: changing a
frequency of measuring said at least one characteristic of said one
or more wireless signals based, at least in part, on said detected
context.
55. An apparatus comprising: means for obtaining measurements of at
least one characteristic of one or more wireless signals acquired
at a mobile station; means for determining a representation of a
signal environment in which said one or more wireless signals were
acquired based, at least in part, on a detected context of said
mobile station; and means for estimating a location of the mobile
station based, at least in part, on a match of the obtained
measurements with the determined representation.
56. An apparatus comprising: a transceiver to obtain measurements
of at least one characteristic of one or more wireless signals
acquired at a mobile station; and one or more processing units to:
determine a representation of a signal environment in which said
one or more wireless signals were acquired based, at least in part,
on a detected context of said mobile station; and estimate a
location of the mobile station based, at least in part, on a match
of the obtained measurements with the determined
representation.
57. The apparatus of claim 56, wherein said one or more processing
units are configured to: maintain a database of expected signal
characteristics associated with locations in an area, determine the
representation by modifying said database of expected signal
characteristics based, at least in part, on said detected context
of said mobile station, and estimate the location by estimating the
location of the mobile station based, at least in part, on a match
of the obtained measurements with the modified expected signal
characteristics.
58. The apparatus of claim 57, wherein said representation of said
signal environment is based, at least in part, on a map of said
area, and wherein said modifying said database is further based, at
least in part, on additional information for said map, said
additional information corresponding to a particular context
classification of said mobile station.
59. The apparatus of claim 58, wherein said particular context
classification of said mobile station comprises a sitting state and
said additional information comprises cubicle partition locations
in said area.
60. The apparatus of claim 56, wherein said representation of said
signal environment comprises a heatmap or a wireless signal
fingerprint.
61. The apparatus of claim 56, wherein said one or more processing
units are configured to determine said representation by selecting
one representation of said signal environment among a plurality of
stored representations of said signal environment based, at least
in part, on said detected context.
62. The apparatus of claim 56, wherein said one or more processing
units are configured to determine said representation by
calculating said representation of said signal environment based,
at least in part, on said detected context.
63. The apparatus of claim 56, wherein said representation of said
signal environment comprises a received signal strength indicator
(RSSI).
64. The apparatus of claim 56, wherein said detected context of
said mobile station comprises a position-and-motion state of said
mobile station.
65. The apparatus of claim 56, wherein said detected context of
said mobile station is based, at least in part, on sensor
measurements from one or more sensors of said mobile station and on
values in a lookup table stored in said mobile station.
66. The apparatus of claim 56, wherein said detected context of
said mobile station is further based, at least in part, on elapsed
time of a state of said mobile station.
67. The apparatus of claim 56, wherein said one or more processing
units are configured to: change a frequency of measuring said at
least one characteristic of said one or more wireless signals
based, at least in part, on said detected context.
68. A non-transitory storage medium comprising machine-readable
instructions stored thereon that are executable by a special
purpose computing device to: obtain measurements of at least one
characteristic of one or more wireless signals acquired at a mobile
station; determine a representation of a signal environment in
which said one or more wireless signals were acquired based, at
least in part, on a detected context of said mobile station; and
estimate a location of the mobile station based, at least in part,
on a match of the obtained measurements with the determined
representation.
Description
BACKGROUND
[0001] 1. Field
[0002] The subject matter disclosed herein relates to wireless
communication systems, and more specifically, to position
determination methods and apparatuses for use with and/or by
wireless mobile stations.
[0003] 2. Information
[0004] GPS and other like satellite positioning systems have
enabled navigation services for mobile handsets in outdoor
environments. Since satellite signals may not be reliably received
or acquired in an indoor environment, different techniques may be
employed to enable navigation services. For example, mobile
stations may obtain a position fix by measuring ranges to three or
more terrestrial wireless access points that are positioned at
known locations. Such ranges may be measured, for example, by
obtaining a MAC ID address from signals received from such access
points and obtaining range measurements to the access points by
measuring one or more characteristics of signals received from such
access points such as, for example, signal strength and round trip
delay.
[0005] A navigation system may provide navigation assistance or
mapped features to a mobile station as it enters a particular area.
For example, in some implementations, an indoor navigation system
may selectively provide assistance information to mobile stations
to facilitate and/or enable location services. Such assistance
information may include, for example, information to facilitate
measurements of ranges to wireless access points at known fixed
locations. For example, "heatmap" data indicating expected
received-signal-strength-indicator (RSSI) or round-trip time (RTT)
values associated with access points may enable a mobile station to
associate signal measurements with locations in an area such as an
indoor location or other location. By matching measured RSSI or RTT
values of acquired signals marked with particular MAC IDs with the
RSSI or RTT values expected for signals marked by these particular
MAC IDs at a specific location, the location of the receiver may be
inferred to be at the specific location.
BRIEF DESCRIPTION OF THE FIGURES
[0006] Non-limiting and non-exhaustive features will be described
with reference to the following figures, wherein like reference
numerals refer to like parts throughout the various figures.
[0007] FIG. 1 is a system diagram illustrating certain features of
a system containing a mobile station, in accordance with an
implementation.
[0008] FIG. 2 is a schematic block diagram of a process to generate
a radio heatmap and to determine a position of a mobile station,
according to an implementation.
[0009] FIG. 3 is a map of a floor of a building sans cubicles,
according to an implementation.
[0010] FIG. 4 is a map of a floor of a building showing cubicles,
according to an implementation.
[0011] FIG. 5 is a flow diagram illustrating a process for
obtaining a position fix of a mobile station, according to an
implementation.
[0012] FIG. 6 is a flow diagram illustrating a process for
modifying a radio heatmap, according to an implementation.
[0013] FIG. 7 is a schematic block diagram illustrating an
exemplary mobile station, in accordance with an implementation.
[0014] FIG. 8 is a schematic block diagram of an example computing
platform.
SUMMARY
[0015] In some implementations, a method may comprise: obtaining
measurements of at least one characteristic of one or more wireless
signals acquired at a mobile station; obtaining information for
classifying a context of a user co-located with the mobile station;
and affecting application of a representation of a signal
environment in which the wireless signals were acquired to the
measurements for obtaining a position fix based, at least in part,
on a classification of the context.
[0016] In other implementations, an apparatus may comprise: means
for obtaining measurements of at least one characteristic of one or
more wireless signals acquired at a mobile station; means for
obtaining information for classifying a context of a user
co-located with the mobile station; and means for affecting
application of a representation of a signal environment in which
the wireless signals were acquired to the measurements for
obtaining a position fix based, at least in part, on a
classification of the context.
[0017] In still other implementations, an apparatus may comprise: a
transceiver to obtain measurements of at least one characteristic
of one or more wireless signals acquired at a mobile station; and
one or more processing units to obtain information to classify a
context of a user co-located with the mobile station, and to affect
application of a representation of a signal environment in which
the wireless signals were acquired to the measurements for
obtaining a position fix based, at least in part, on a
classification of the context.
[0018] In other implementations, an article may comprise: a
non-transitory storage medium comprising machine-readable
instructions stored thereon that are executable by a special
purpose computing device to: obtain measurements of at least one
characteristic of one or more wireless signals acquired at a mobile
station; obtain information to classify a context of a user
co-located with the mobile station; and affect application of a
representation of a signal environment in which the wireless
signals were acquired to the measurements for obtaining a position
fix based, at least in part, on a classification of the
context.
[0019] In yet other implementations, a method may comprise:
obtaining measurements of at least one characteristic of one or
more wireless signals acquired at a mobile station; determining a
representation of a signal environment in which the wireless
signals were acquired based, at least in part, on a detected
context of the mobile station; estimating a location of the mobile
station based, at least in part, on a match of the obtained
measurements with the determined representation; and selecting one
representation of the signal environment among a plurality of
stored representations of the signal environment based, at least in
part, on the detected context.
[0020] In other implementations, an apparatus may comprise: means
for maintaining a database of expected signal characteristics
associated with locations in an area; means for obtaining
measurements of at least one characteristic of one or more wireless
signals acquired at a mobile station; means for modifying the
expected signal characteristics based, at least in part, on a
detected context of the mobile station; and means for estimating a
location of the mobile station based, at least in part, on a match
of the obtained measurements with the modified expected signal
characteristics.
[0021] In other implementations, an apparatus may comprise: a
transceiver to obtain measurements of at least one characteristic
of one or more wireless signals acquired at a mobile station; and
one or more processing units to: maintain a database of expected
signal characteristics associated with locations in an area; modify
the expected signal characteristics based, at least in part, on a
detected context of the mobile station; and estimate a location of
the mobile station based, at least in part, on a match of the
obtained measurements with the modified expected signal
characteristics.
[0022] In still other implementations, an article may comprise: a
non-transitory storage medium comprising machine-readable
instructions stored thereon that are executable by a special
purpose computing device to: maintain a database of expected signal
characteristics associated with locations in an area; obtain
measurements of at least one characteristic of one or more wireless
signals acquired at a mobile station; modify the expected signal
characteristics based, at least in part, on a detected context of
the mobile station; and estimate a location of the mobile station
based, at least in part, on a match of the obtained measurements
with the modified expected signal characteristics.
DETAILED DESCRIPTION
[0023] Reference throughout this specification to "one example",
"one feature", "an example" or "one feature" means that a
particular feature, structure, or characteristic described in
connection with the feature and/or example is included in at least
one feature and/or example of the described subject matter. Thus,
the appearances of the phrase "in one example", "an example", "in
one feature", or "a feature" in various places throughout this
specification are not necessarily all referring to the same feature
and/or example. Furthermore, the particular features, structures,
or characteristics may be combined in one or more examples and/or
features, and/or may be omitted from one or more embodiments and/or
implementations, and are not limiting to the scope of the claims or
the scope of this disclosure.
[0024] As used herein, a mobile station (MS) refers to a device
such as a cellular or other wireless communication device, personal
communication system (PCS) device, personal navigation device,
Personal Information Manager (PIM), Personal Digital Assistant
(PDA), laptop or other suitable mobile station which is capable of
receiving wireless communications. The term "mobile station" is
also intended to include devices which communicate with a personal
navigation device (PND), such as by short-range wireless, infrared,
wireline connection, or other connection--regardless of whether
satellite signal reception, assistance data reception, and/or
position-related processing occurs at the device or at the PND.
Also, "mobile station" is intended to include all devices,
including wireless communication devices, computers, laptops, etc.
which are capable of communication with a server, such as via the
Internet, WiFi, or other network, and regardless of whether
satellite signal reception, assistance data reception, and/or
position-related processing occurs at the device, at a server, or
at another device associated with the network. Any operable
combination of the above are also considered a "mobile
station."
[0025] In some implementations, an indoor navigation system may
selectively provide assistance information to an MS to facilitate
and/or enable location services. Such assistance information may
include, for example, information to facilitate measurements of
ranges to wireless access points at known fixed locations. For
example, "heatmap" data indicating expected
received-signal-strength-indicator (RSSI) values or round-trip
times (RTT) associated with access points may enable an MS to
associate signal measurements with locations in an indoor area.
Additionally, such assistance data may also include routeability
information indicative of feasible/navigable paths in an indoor
area covered by a digital map.
[0026] In a particular implementation, assistance information may
be provided to an MS from a local server through wireless
communication links. The MS may then locally store received
assistance information in a local memory. It should be understood,
however, that in larger indoor areas with multiple access points
and feasible routes, such assistance information may be quite
voluminous so as to tax available bandwidth in wireless
communication links and data storage space on mobile stations.
[0027] According to an embodiment, assistance information may be
provided to an MS in a compressed format. For example, such
assistance information may be provided as metadata along with
metadata included in a digital map. Here, grid points, for example,
may be laid over locations in an indoor interval at uniform spacing
(e.g., two-feet separation of neighboring grid points). Grid points
may comprise a set of discrete points obtained by superimposing
uniformly-spaced points on a map in a grid pattern, though claimed
subject matter is not so limited. Heatmap or connectivity
information may be provided for individual grid points in metadata
organized by rows, for example. In one implementation, a single row
may include values for RSSI, RSSI variances (e.g., standard
deviation or other uncertainty characteristics of RSSI values),
RTT, and RTT variances for associated access points. Here, the
access points may be represented by their MAC ID addresses, for
example. In one particular implementation, an RSSI heatmap value
and associated variance may be represented by one byte each while a
delay heatmap value (e.g., corresponding to round-trip time
measurements) and associated variance may be represented by two
bytes each, though claimed subject matter is not limited in this
respect. Additionally, a single field may indicate connectivity
(i.e., a feasible path) with adjoining grid points (e.g., Boolean 1
or 0 to indicate whether there is connectivity with an associated
grid point). Accordingly, heatmap data indicating expected RSSI or
RTT values associated with access points may enable an MS to
associate signal measurements with locations in an indoor area. By
matching measured RSSI or RTT values of acquired signals marked
with particular MAC IDs with expected RSSI or RTT values for
signals marked by these particular MAC IDs at a specific location,
the location of the MS may be inferred to be at the specific
location.
[0028] In one implementation, an MS may determine RTT values by
transmitting a probe signal and measuring an elapsed time until the
MS receives an acknowledging response from one or more access
points. For example, an MS may identify individual access points
using a MAC ID of the individual access points. An MS may infer its
distance to a particular access point based, at least in part, on
an RTT value comprising the elapsed time between probe signal
transmission and a probe signal response from the particular access
point. Such an elapsed time may comprise travel time of the probe
signal and the probe signal response in addition to a process delay
at the access point. For example, such a process delay may include
a time that it takes for an access point to receive a probe signal
and to process and transmit a probe response signal. In some cases,
RTT values may be affected by multi-path signals, wherein an MS may
receive a probe response signal from an access point via more than
one path. In such a case, different RTT values may arise for
different signal paths. In one implementation, the shortest signal
path (e.g., the smallest RTT value) or the strongest (e.g., highest
signal amplitude) received probe signal may be considered to be
associated with a line-of-sight path, which an MS may use to infer
distance to an access point.
[0029] A radio heatmap model, such as that described above, for
example, may be based, at least in part, on measured RSSI or RTT
values of an acquired signal that are determined, at least in part,
by a range from a receiver to a transmitter. In one particular
implementation, it may be recognized that measured RSSI or RTT
values of an acquired signal may be affected by factors other than
a range from a receiver to a transmitter. In one example, a
measured RSSI or RTT value may also be affected by a "context" of
an MS or a user co-located with the MS. Examples of different
contexts may include, for example, an MS being held in a user's
hand while the user is walking, an MS in a user's front shirt
pocket, an MS in a purse or handbag, an MS in a holster, an MS with
an attached battery pack, or whether a user is sitting with an MS
in a cubicle environment, just to provide a few examples. In one
example, a particular context of an MS may affect RTT values by
affecting whether or not, and to what extent probe signals or probe
response signals travel via multiple paths (e.g., multi-path). In
another example, probe response signals may travel through
different materials for different contexts of an MS. Accordingly,
probe response signals may be attenuated differently for the
different materials.
[0030] Different contexts associated with an MS may be detected by,
for example, processing signals received from one or more sensors
on the MS. Such sensors may comprise, for example, ambient light
sensors, inertial sensors (e.g., accelerometers, gyroscopes,
magnetometers), temperature sensors, or a microphone, just to name
a few examples. Alternatively, different contexts may be determined
other than at an MS, such as at a land-based server, for example.
In such a case, information regarding contexts may be wirelessly
provided to an MS. In one example, context-related information
provided to a server and matched in a point-of-interest database
may allow for determining the context of "shopping in aisle". Such
context information may then be provided to an MS. In another
example, if a person is using a computer at the person's desk, such
computer use may allow for a determination of a context of "sitting
at desk". Such context information may then be provided to an
MS.
[0031] Various techniques are described herein which may be
implemented in one or more land-based computing platforms or an MS
to affect or alter an application of a radio heatmap, which may
comprise a particular example of a representation of a signal
environment in which the wireless signals were acquired, to
determining a position fix for an MS. In one example
implementation, application of a radio heatmap for obtaining a
position fix for an MS may be affected or altered based, at least
in part, on a classified or determined context of the MS (or of a
user in possession of the MS). Also, wireless signal fingerprints
may also be affected or altered based, at least in part, on a
classified or determined context of the MS. Here, wireless signal
fingerprints may be used in a process of recording ground truths,
providing an MS of its location, or storing wireless signals
transmitted by one or more wireless beacon devices. Such a process
performed at multiple locations in a building, for example, may be
called "fingerprinting a building". Once such fingerprints are
collected, a newly-introduced MS may determine its most likely
location by comparing what signals it receives to a fingerprint
database. Those of skill in the art will appreciate that techniques
described herein may be implemented to affect or alter an
application of a representation of a signal environment other than
a heatmap or fingerprint. Thus, while the description herein may
refer to a heatmap and/or a fingerprint, embodiments are not
limited to those representations.
[0032] RSSI or RTT values of an acquired transmission signal may
comprise parameters that correspond to signal loss and may indicate
a distance traveled by the transmission signal. For example, RTT
may increase as the travel distance of a signal increases. In
another example, RSSI may decrease as the travel distance of a
signal increases. In some cases, one or more propagation parameters
may be used to predict or infer, at least in part, signal loss over
distance. Such signal loss, for example, may comprise exponential
or linear signal degradation, though claimed subject matter is not
so limited.
[0033] A radio heatmap may comprise a collection of heatmap values
corresponding to expected RSSI or RTT values at particular
locations (e.g., grid points) represented by the radio heatmap. For
example, a radio heatmap may comprise heatmap values individually
corresponding to particular grid points or relatively small areas
of a region represented by a map of the region. Such a map may
comprise a plurality of electronic signals representative of
physical locations of a region and expected RSSI or RTT values for
the physical locations. In a particular example, an RSSI heatmap of
a shopping mall or office building may comprise a map of the
shopping mall or office building including expected RSSI
measurements for various locations (e.g., grid points) of the
shopping mall or office building.
[0034] In some implementations, an MS may receive navigation
assistance data from a navigation system (e.g., located at a
land-based server) as the MS enters a particular area. Such
navigation assistance data may comprise a digital electronic map,
for example. A navigation system may comprise an indoor navigation
application, which may include one or more maps to show features of
indoor structures such as doors, hallways, entry ways, walls, or
points of interest (e.g., bathrooms, pay phones, room names,
stores). Navigation assistance data may further include, for
example, a radio heatmap to facilitate measurements of ranges to
wireless access points positioned at known fixed locations. As
mentioned above, a radio heatmap may comprise information
indicating, for a location on a map, expected RSSI or RTT values
associated with particular access points among a plurality of
access points. Accordingly, by obtaining a digital electronic map,
and by determining a current location by measuring RSSI or RTT
values and using a radio heatmap, an MS may digitally overlay the
current location of the MS on the map, for example. A digital
electronic map or a radio heatmap may be stored at a server to be
accessible by an MS through selection of a URL, for example.
[0035] In an implementation, measurements of at least one
characteristic of one or more wireless signals acquired at an MS
may be obtained and used in a process of obtaining a position fix
of an MS. For example, a measurement of one or more characteristics
of wireless signals received at an MS may comprise RSSI values, as
described above. In one application, a context of a user co-located
with the MS or a context of the MS may be determined or classified.
For example, a context of a user may comprise a state of sitting or
walking. In one case, an MS co-located with a sitting user may
measure an RSSI value based, at least in part, on received probe
response signals that travel through cubicle partitions. In such a
case, such probe response signals may be additionally attenuated
compared to probe response signals received by an MS co-located
with a standing user. Accordingly, all other things being equal, a
measured RSSI value may be lower for a "sitting" context compared
to a "standing" context. In the case of cubicles, for example,
sitting may introduce additional signal attenuation. Thus, to
account for a "sitting" context, an expected RSSI value may be
reduced. In other words, an MS may use a radio heatmap of RSSI
values to determine a location of the MS. However, before such a
radio heatmap is used, at least a portion of the radio heatmap may
be altered or modified based, at least in part, on the context of
the user or the MS co-located with the user. An existing heatmap
need not be modified in all embodiments; for example, a new heatmap
may be calculated using additional data (e.g., using not just
information regarding a building's walls, but also using cubicle
walls or other stanchions if a user is seated). For example, at a
particular location and for a particular access point associated
with a MAC ID, a radio heatmap may indicate an expected value for
RSSI at the particular location. Such an expected value may be
reduced, however, if an MS receiving the RSSI is in a pocket of a
user, as opposed to being held in the user's hand. In addition to
affecting RSSI values, different contexts may also affect variance
or standard deviation of RSSI values, RTT values, and variance or
standard deviation of RTT values, for example. RTT measurements may
be affected by attenuation. For example, access points may use an
observed RSSI to trigger changes in signal transmission
characteristics (e.g., timing, signal strength). This may generate
undesirable multiple peaks in a probe response, so that time of
arrival of particular signals may be relatively difficult to
determine. For example, if an MS is in a relatively poor signal
state (e.g., by being in a carrying bag), the MS may activate
different algorithms to detect or mitigate different types of
signaling from an access point.
[0036] In a particular implementation, a context of a user or MS
may determine a rate at which at least one characteristic of one or
more wireless signals may be measured. For example, RSSI may be
measured relatively often if the context of a user comprises
"walking", whereas RSSI may be measured less often if the context
of a user comprises "sitting". Changing a frequency of measuring at
least one characteristic of one or more wireless signals based, at
least in part, on context may provide a number of advantages. For
example, an MS may change position at a relatively slow rate for a
"sitting" context. In this case, a relatively low rate of RSSI
measurements may be sufficient to obtain a position fix of the MS.
Accordingly, battery life of the MS may be extended by reducing a
rate of RSSI measurements. On the other hand, an MS may change
position at a relatively fast rate for a "walking" or "running"
context. In this case, a relatively high rate of RSSI measurements
may be desirable to obtain a position fix of the MS. Accordingly,
an MS may increase a rate of RSSI measurements to improve an
estimate of an MS location. A context of a user may also affect
particle filter operation such as by changing an operation of a
particle filter by changing a quantity of new particles or a
velocity of particle propagation, for example.
[0037] In another particular implementation, affecting application
of a radio heatmap to RSSI measurements for obtaining a position
fix of an MS may be based, at least in part, on one or more
behaviors of a user co-located with the MS. For example, whether a
user occupies a particular room several days per week during a
lunch hour may be considered while determining how to apply a radio
heatmap to RSSI measurements. In one implementation, a memory
device may store time-stamped position fixes to maintain a record
of where a user may have been located at different times. An
application may be executed by a processor, for example, to use
such time-stamped location information to search for behavioral
patterns of a user that may indicate or predict a context for the
user.
[0038] In an implementation, a technique for detecting a context of
an MS or of a user in possession of the MS may involve processing
sensor measurements from one or more sensors included in the MS.
Sensors may comprise any of a number of sensor types, such as
inertial sensors (e.g., accelerometers, gyroscopes, magnetometers,
etc.) and environment sensors (e.g., temperature sensors,
microphones, barometric pressure sensors, ambient light sensors,
camera imager, etc.), as discussed above. Such sensors may be used
to estimate a location or motion state of the MS. A lookup table
stored in the MS may be used for adjusting expected signal
characteristics based, at least in part, on a determined context of
the MS. Such a Table may include empirical data corresponding to a
plurality of context classifications, as explained below.
[0039] In certain implementations, as shown in FIG. 1, an MS 100
may receive or acquire SPS signals 159 from SPS satellites 160. In
some embodiments, SPS satellites 160 may be from one global
navigation satellite system (GNSS), such as the GPS or Galileo
satellite systems. In other embodiments, the SPS Satellites may be
from multiple GNSS such as, but not limited to, GPS, Galileo,
Glonass, or Beidou (Compass) satellite systems. In other
embodiments, SPS satellites may be from any one several regional
navigation satellite systems (RNSS') such as, for example, WAAS,
EGNOS, QZSS, just to name a few examples.
[0040] In addition, the MS 100 may transmit radio signals to, and
receive radio signals from, a wireless communication network. In
one example, MS 100 may communicate with a cellular communication
network by transmitting wireless signals to, or receiving wireless
signals from, a base station transceiver 110 over a wireless
communication link 123. Similarly, MS 100 may transmit wireless
signals to, or receiving wireless signals from local transceivers
115 over a wireless communication link 125. In a particular
implementation, one or more local transceivers 115 may be
configured to communicate with MS 100 at a shorter range over
wireless communication link 123 than at a range enabled by base
station transceiver 110 over wireless communication link 123. For
example, local transceivers 115 may be positioned in an indoor
environment. Local transceivers 115 may provide access to a
wireless local area network (WLAN, e.g., IEEE Std. 802.11 network)
or wireless personal area network (WPAN, e.g., Bluetooth network).
In another example implementation, local transceivers 115 may
comprise a femto cell transceiver capable of facilitating
communication on link 125 according to a cellular communication
protocol. Of course, it should be understood that these are merely
examples of networks that may communicate with an MS over a
wireless link, and claimed subject matter is not limited in this
respect.
[0041] In a particular implementation, base station transceiver 110
and local transceivers 115 may communicate with servers 140, 150
and 155 over a network 130 through links 145. Here, network 130 may
comprise any combination of wired or wireless links. In a
particular implementation, network 130 may comprise Internet
Protocol (IP) infrastructure capable of facilitating communication
between MS 100 and servers 140, 150 or 155 through local
transceivers 115 or base station transceiver 110. In another
implementation, network 130 may comprising cellular communication
network infrastructure such as, for example, a base station
controller or master switching center to facilitate mobile cellular
communication with MS 100.
[0042] In particular implementations, and as discussed below, MS
100 may have circuitry and processing resources capable of
computing a position fix or estimated location of MS 100. For
example, MS 100 may compute a position fix based, at least in part,
on pseudorange measurements to four or more SPS satellites 160.
Here, MS 100 may compute such pseudorange measurements based, at
least in part, on pseudonoise code phase detections in signals 159
acquired from four or more SPS satellites 160. In particular
implementations, MS 100 may receive from server 140, 150 or 155
positioning assistance data to aid in the acquisition of signals
159 transmitted by SPS satellites 160 including, for example,
almanac, ephemeris data, Doppler search windows, just to name a few
examples.
[0043] In other implementations, MS 100 may obtain a position fix
by processing signals received from terrestrial transmitters fixed
at known locations (e.g., such as base station transceiver 110)
using any one of several techniques such as, for example, advanced
forward trilateration (AFLT) and/or observed time difference of
arrival (OTDOA). In these particular techniques, a range from MS
100 may be measured to three or more of such terrestrial
transmitters fixed at known locations based, at least in part, on
pilot signals transmitted by the transmitters fixed at known
locations and received at MS 100. Here, servers 140, 150 or 155 may
be capable of providing positioning assistance data to MS 100
including, for example, locations and identities of terrestrial
transmitters to facilitate positioning techniques such as AFLT and
OTDOA. For example, servers 140, 150 or 155 may include a base
station almanac (BSA) which indicates locations and identities of
cellular base stations in a particular region or regions.
[0044] In particular environments such as indoor environments or
urban canyons, MS 100 may not be capable of acquiring signals 159
from a sufficient number of SPS satellites 160 or perform AFLT or
OTDOA to compute a position fix. Alternatively, MS 100 may be
capable of computing a position fix based, at least in part, on
signals acquired from local transmitters (e.g., femto cells or WLAN
access points positioned at known locations), such as access point
310 shown in FIG. 3. For example, MSs may obtain a position fix by
measuring ranges to three or more indoor terrestrial wireless
access points which are positioned at known locations, as shown in
FIG. 2. Such ranges may be measured, for example, by obtaining a
MAC ID address from signals received from such access points and
obtaining range measurements to the access points by measuring one
or more characteristics of signals received from such access points
such as, for example, received signal strength (RSSI) or round trip
time (RTT). In alternative implementations, MS 100 may obtain an
indoor position fix by applying characteristics of acquired signals
to a radio heatmap indicating expected RSSI or RTT values at
particular locations in an indoor area.
[0045] In particular implementations, MS 100 may receive
positioning assistance data for indoor positioning operations from
servers 140, 150 or 155. For example, such positioning assistance
data may include locations and identities of transmitters
positioned at known locations to enable measuring ranges to these
transmitters based, at least in part, on a measured RSSI and/or
RTT, for example. Other positioning assistance data to aid indoor
positioning operations may include radio heatmaps, locations and
identities of transmitters, routeability graphs, just to name a few
examples. Other assistance data received by the MS may include, for
example, local maps of indoor areas for display or to aid in
navigation. Such a map may be provided to MS 100 as MS 100 enters a
particular indoor area. Such a map may show indoor features such as
doors, hallways, entry ways, walls, etc., points of interest such
as bathrooms, pay phones, room names, stores, etc. By obtaining and
displaying such a map, an MS may overlay a current location of the
MS (and user) over the displayed map.
[0046] In one implementation, a routeability graph and/or digital
map may assist MS 100 in defining feasible areas for navigation
within an indoor area and subject to physical obstructions (e.g.,
walls) and passage ways (e.g., doorways in walls). Here, by
defining feasible areas for navigation, MS 100 may apply
constraints to aid in the application of filtering measurements for
estimating locations and/or motion trajectories according to a
motion model (e.g., according to a particle filter and/or Kalman
filter). In addition to measurements obtained from the acquisition
of signals from local transmitters, according to a particular
embodiment, MS 100 may further apply a motion model to measurements
or inferences obtained from inertial sensors (e.g., accelerometers,
gyroscopes, magnetometers, etc.) and/or environment sensors (e.g.,
temperature sensors, microphones, barometric pressure sensors,
ambient light sensors, camera imager, etc.) in estimating a
location or motion state of MS 100.
[0047] According to an embodiment, MS 100 may access indoor
navigation assistance data through servers 140, 150 or 155 by, for
example, requesting the indoor assistance data through selection of
a universal resource locator (URL). In particular implementations,
servers 140, 150 or 155 may be capable of providing indoor
navigation assistance data to cover many different indoor areas
including, for example, floors of buildings, wings of hospitals,
terminals at an airport, portions of a university campus, areas of
a large shopping mall, just to name a few examples. Also, memory
resources at MS 100 and data transmission resources may make
receipt of indoor navigation assistance data for all areas served
by servers 140, 150 or 155 impractical or infeasible, a request for
indoor navigation assistance data from MS 100 may indicate a rough
or course estimate of a location of MS 100. MS 100 may then be
provided indoor navigation assistance data covering areas including
and/or proximate to the rough or course estimate of the location of
MS 100.
[0048] In one particular implementation, a request for indoor
navigation assistance data from MS 100 may specify a location
context identifier (LCI). Such an LCI may be associated with a
locally defined area such as, for example, a particular floor of a
building or other indoor area which is not mapped according to a
global coordinate system. In one example server architecture, upon
entry of an area, MS 100 may request a first server, such as server
140, to provide one or more LCIs covering the area or adjacent
areas. Here, the request from the MS 100 may include a rough
location of MS 100 such that the requested server may associate the
rough location with areas covered by known LCIs, and then transmit
those LCIs to MS 100. MS 100 may then use the received LCIs in
subsequent messages with a different server, such as server 150,
for obtaining navigation assistance relevant to an area
identifiable by one or more of the LCIs as discussed above (e.g.,
digital maps, locations and identifies of beacon transmitters,
radio heatmaps or routeability graphs).
[0049] FIG. 2 is a schematic block diagram of a process 200 to
generate a radio heatmap and to determine a position of an MS,
according to an implementation. In process 200, an application of a
radio heatmap to RSSI measurements received at an MS for obtaining
a position fix may be affected based, at least in part, on a
classified context of the MS or a user co-located with the MS.
Process 200 may comprise a process portion 210 that may be
performed by the MS or another entity, such as servers 140, 150 or
155 shown in FIG. 1, for example. Further, process portion 210 may
be performed "off-line" during a time prior to a process of
determining a position fix for an MS. For example, as explained in
detail below, actions corresponding to blocks 220, 222, 224, and
226 may be performed independently of blocks 230, 232, 234, 236,
and 240, though claimed subject matter is not so limited.
[0050] Process portion 210 may include block 220, where model
parameters may be based, at least in part, on a general structure
of a building. Model parameters, may comprise dimensions or sizes
or building features, such as entryways, hallways, rooms, or a
floor plan, just to name a few examples. Propagation parameters
corresponding to block 222 and the model parameters of a building
may be used to generate values of a radio heatmap for the building
at block 226. Here, such propagation parameters may be used to
predict or infer, at least in part, signal loss over distance
though air, walls, or other building materials, for example. Such
signal loss may comprise exponential or linear signal degradation,
though claimed subject matter is not so limited. Corresponding to
block 224, wall characterization of the building may also be used
to generate values of a radio heatmap at block 226. For example,
such wall characterization may comprise a mapping of layout or
locations of walls or partitions that at least partially separate
rooms or hallways from one another. In one implementation,
descriptions of cubicle partitions that separate cubicle spaces
need not be included in such wall information since cubicle
partitions may be considered in a separate process, as explained
below.
[0051] At block 230, a context of an MS or a user co-located with
the MS may be determined or classified. Some examples of context of
a user co-located with an MS include a user sitting, standing,
walking, holding the MS in a pocket or bag, storing the MS in a
carry case, located in an open area of a floor space, located in a
cubicle area and standing, located in a cubicle area and sitting,
and so on. A context may be determined or classified based, at
least in part, on sensor measurements from one or more sensors of
the MS to estimate a location or motion state of an MS or a user
co-located with the MS. For example, sensors may comprise inertial
or position sensors, such as, an accelerometer, a gyroscope, a
magnetometer, a compass, a gravitometer, and so on. Sensors may
also comprise environment sensors, such as a temperature sensor, an
audio sensor, a proximity sensor, a microphone, a barometric
pressure sensor, an ambient light sensor, a camera imager, and so
on. Some examples of techniques that may be used to estimate a
location or motion state of an MS or a user co-located with the MS
using sensor measurements are described as follows. For a first
example, inertial or position sensor measurements may be used to
determine an orientation or location of an MS. Some examples may
include being in a pocket or carry case, resting on a desktop,
being held upright in a user's hand, being in motion, and so on.
For example, combined with other measurements, an MS oriented on
its side may indicate that the MS is resting on a table or desk if
the MS is not in motion. In another example, environment sensor
measurements may be used to determine a location of an MS. Combined
with other measurements, an MS in a location characterized by
relatively low ambient sound volume (e.g., as measured by a
microphone) may indicate that the MS is in a cubicle area as
opposed to being in a reception area. Of course, such details of
sensor applications are merely examples, and claimed subject matter
is not so limited.
[0052] In one implementation, a context of an MS or a user
co-located with the MS may be classified based, at least in part,
on an elapsed time that a user or an MS is in a particular location
or motion state. For example, if a motion state of a user comprises
non-movement for more than several minutes, it may be determined
that the user is sitting as opposed to standing. In one particular
implementation, a determination may be made as to whether a user is
sitting in an open area or a cubicle area.
[0053] At block 232, in one implementation, sensor measurements may
be compared to values in a lookup table stored in an MS to classify
a context of the MS or a user co-located with the MS. Classifying a
context may be based, at least in part, on sensor measurements, as
described above. For example, a lookup table may comprise values
based, at least in part, on empirical data comprising sensor
measurements. A lookup table may correlate such empirical data with
corresponding context classifications. For example, referring to a
lookup table, one particular range of sensor measurements may
indicate that a context of an MS is "sitting", while another
particular range of sensor measurements may indicate that a context
of the MS is "standing".
[0054] In another implementation, a lookup table may used in a
process to adjust heatmap values based, at least in part, on a
classified context. For example, process 200 may include block 236
where a context-based adjustment of heatmap values from block 226
may be performed. For example, as indicated above, such heatmap
values may have been generated at an earlier time (e.g., off-line)
based, at least in part, on building parameters (e.g., block 220)
and propagation parameters (e.g., block 222). A context, as
determined at block 230, may be used with a lookup table to
determine how to adjust signal characteristics. In one example,
such a lookup table may be used to determine by how much a quantity
of RSSI is to be added or subtracted to or from an expected RSSI
value based, at least in part, on context of an MS. For another
example, if a context of an MS is "sitting", then a lookup table
may indicate that 7.0 dB is to be subtracted from an expected
signal characteristic of the heatmap provided from process portion
210. In another example, if a context of an MS is "located in a
pocket", then a lookup table may indicate that 4.0 dB is to be
subtracted from an expected signal characteristic of the heatmap.
Of course, such details of a lookup table are merely examples, and
claimed subject matter is not so limited.
[0055] In one implementation, a building template or map, as
introduced at block 220, used to generate a heatmap (e.g., block
226) may be augmented with additional building map information, as
at block 234. For example, if the context of a user comprises
"sitting", then a map of a building may be augmented with
descriptions (e.g., locations or dimensions) of cubicle partitions
that separate cubicle spaces in the building so as to account for a
possibility that the user is located in a cubicle. In such a case,
cubicle partitions may decrease RSSI values of a heatmap. Such a
decrease may be due, at least in part, to attenuation of signals
transmitted by access points. Thus, cubicle partitions, in addition
to walls of the building, may be considered in determining expected
signal characteristics if a user is sitting in a cubicle, for
example. Accordingly, at block 236, heatmap values based, at least
in part, on walls of the building may be adjusted to also be based,
at least in part, on cubicle partitions, for example.
[0056] A heatmap comprising original expected signal
characteristics from block 226 and expected signal characteristics
adjusted at block 236 may be provided to a positioning engine at
block 240. Such a positioning engine may obtain a position fix of
an MS by attempting to match heatmap values measured at the MS to
the adjusted expected signal characteristics.
[0057] FIG. 3 is a map 300 of a floor of a building sans cubicles,
according to an implementation. Map 300 may show, among other
things, a number of hallways 305 and walls 320. An area 330 may
comprise a relatively open area void of walls. In an
implementation, a heatmap may be generated based, at least in part,
on map 300. For example, at block 226 in process 200, a heatmap may
be generated based, at least in part, on a map such as map 300.
[0058] In an implementation, a method for obtaining a position fix
of an MS may comprise obtaining measurements of RSSI at an MS,
classifying a context of a user co-located with the MS, and
affecting application of a heatmap to the RSSI measurements based,
at least in part, on the classified context. Such a heatmap may be
based, at least in part, on particular map information (e.g.,
locations of walls, halls, rooms, and so on), as discussed above.
Affecting an application of such a heatmap may be further based, at
least in part, on additional map information corresponding to a
particular context classification of the user. For example, such a
particular context classification of a user may comprise a sitting
state and such additional map information may comprise locations of
cubicle partitions. A particular context classification of a user
may further comprise a location of the sitting state. In one
implementation, additional map information may be provided by a map
that includes information regarding locations of halls, walls,
rooms, and cubicles. For example, FIG. 4 is a map 400 of a floor of
a building showing cubicles, according to an implementation. Map
400 may be similar to map 300 except that map 400 may include
mapping of cubicles. For example, map 400 may show, among other
things, a number of hallways 305, walls 320, a conference table
410, and cubicles 435. In map 300, area 330 is shown to comprise a
relatively open area. However, in map 400, area 330 is shown to
comprise a number of cubicles 435.
[0059] In an implementation, a heatmap may be generated based, at
least in part, on map 400. In this case, cubicle partitions may be
considered while generating such a heatmap. On the other hand, a
heatmap generated based, at least in part, on map 300 may not
consider cubicle partitions while generating a heatmap. A heatmap
based, at least in part, on map 300 may be suitably applied to
estimating a location of a user co-located with an MS if the user
is standing. However, a heatmap based, at least in part, on map 400
may be suitably applied to estimating a location of a user
co-located with an MS if the user is sitting. For example, if a
user 440 is sitting in a cubicle (co-located with an MS), a signal
transmitted from an AP may be attenuated by cubicle partitions (in
addition to walls, etc.) before being received by an MS. This may
be because the MS may be relatively near the floor of the building,
where cubicle partitions may block a line of sight between an AP
transmitting an RSSI signal and the MS. Accordingly, knowledge of
cubicle locations and dimensions provided by map 400 may facilitate
consideration of RSSI signal attenuation by cubicle partitions for
obtaining a position fix of the MS. Thus, RSSI values of a heatmap
based, at least in part, on map 300 (sans cubicles) may be adjusted
for sitting user 440 by using information provided by map 400 (with
cubicles).
[0060] On the other hand, if a user 450 is sitting in a relatively
open area, as opposed to a cubicle area (such as where user 440
sits), then map 400 may provide cubicle locations and dimensions
showing that user 450 is located in a relatively open area.
Accordingly, RSSI values of a heatmap based, at least in part, on
map 300 (sans cubicles) need not be adjusted for sitting user
450.
[0061] FIG. 5 is a flow diagram illustrating a process 500 for
obtaining a position fix of a mobile station, according to an
implementation. Process 500 may involve determining whether a user
of an MS is sitting in or near a cubicle. If a user is located in
or near a cubicle, expected RSSI values of a heatmap may be
decreased to account for signals from access points being
attenuated by cubicle partitions. In such a case, a database of
heatmap values based, at least in part, on walls of a building may
be adjusted to also be based, at least in part, on cubicle
partitions, for example. Process 500 may be performed by an MS,
such as MS 100, or a server, such as 140, shown in FIG. 1, for
example. At block 510, an MS or a server, for example, may maintain
a database of expected signal characteristics for an area. In an
implementation, such a database may comprise a heatmap of expected
RSSI values for an area such as an office building or shopping
mall, just to name a few examples. Such a database may have been
generated at an earlier time, based, at least in part, on a map of
walls, rooms, partitions, or hallways of the area.
[0062] At block 520, a context of an MS or a user co-located with
the MS may be determined. As mentioned above, examples of different
contexts may include, for example, an MS being held in a user's
hand while the user is walking, an MS in a user's front shirt
pocket, an MS in a purse or handbag, or whether a user is sitting
with an MS in a cubicle environment, just to provide a few
examples. Different contexts associated with an MS may be detected
by, for example, processing signals received from one or more
sensors on the MS. Such sensors may comprise, for example, ambient
light sensors, inertial sensors, temperature sensors, or a
microphone, just to name a few examples.
[0063] At diamond 530, a determination may be made as to whether
the determined context of the MS (or the context of a user
co-located with the MS) corresponds to a user co-located with the
MS in a "sitting" state. In other words, with respect to the
context of the MS, it may be determined whether or not the user is
sitting. If not, then process 500 may proceed to block 545, where
an expected signal characteristic, such as an expected RSSI value,
may be changed or modified based, at least in part, on the context
of the MS determined at block 520. Process 500 may then proceed to
block 560, where a location of the MS may be estimated based, at
least in part, on the database of expected signal characteristics
and on one or more modified values of the database, as modified at
block 545, for example.
[0064] On the other hand, if it is determined that the user is
sitting, then process 500 may proceed to diamond 540, where a
determination may be made as to whether the MS (or the user
co-located with the MS) is located in a cubicle portion of the
area. If not, then process 500 may proceed to block 545, where an
expected signal characteristic, such as an expected RSSI value, may
be modified based, at least in part, on the context of the MS
determined at block 520. As discussed above, process 500 may then
proceed to block 560, where a location of the MS may be estimated.
On the other hand, if it is determined that the MS or user is
located in a cubicle portion of the area, then process 500 may
proceed to block 550, where cubicle information may be incorporated
into the database of expected signal characteristics. For example,
cubicle information may comprise descriptions (e.g., locations or
dimensions) of cubicle partitions that separate cubicle spaces. As
discussed above, in a case where a user is located in or among
cubicles, RSSI values may be attenuated by cubicle partitions in
addition to walls of a building.
[0065] Process 500 may then proceed to block 545, where the
database of expected signal characteristics for an area may
incorporate cubicle information. Accordingly, such expected signal
characteristics (e.g., an expected RSSI value) may be modified
based, at least in part, on the context of the MS determined at
block 520. Process 500 may then proceed to block 560, where a
location of the MS may be estimated based, at least in part, on the
database of expected signal characteristics and on one or more
modified values of the database, as modified at block 545, for
example. Of course, such details of process 500 are merely
examples, and claimed subject matter is not so limited.
[0066] FIG. 6 is a flow diagram illustrating a process 600 for
modifying a heatmap, according to an implementation. Process 600
may be performed by an MS, such as MS 100, or a server, such as
140, shown in FIG. 1, for example. At block 610, measurements of at
least one characteristic of one or more wireless signals acquired
at an MS may be obtained and used in a process of obtaining a
position fix of an MS. For example, a measurement of one or more
characteristics of wireless signals received at an MS may comprise
RSSI values. At block 620, a context of a user co-located with the
MS or a context of the MS may be determined or classified. For
example, a context of a user may a state of sitting or walking. In
one implementation, block 620 may be performed on-the-fly, wherein
a context of a user may be classified during a process of obtaining
a position of the MS. At block 630, an application of a heatmap to
the measurements to obtain a position fix may be affected based, at
least in part, on the classified context. For example, an MS may
apply a heatmap to measured RSSI to determine its location.
However, before such a heatmap is applied, the heatmap may be
altered or modified based, at least in part, on the context of the
user or on the context of the MS co-located with the user. For
example, at a particular location, a heatmap may indicate an
expected value for RSSI at the particular location. Such an
expected value may be reduced, however, if an MS receiving the RSSI
is in a pocket of a user, as opposed to being held in the user's
hand. Of course, such details of process 500 are merely examples,
and claimed subject matter is not so limited.
[0067] FIG. 7 is a schematic diagram of an MS according to an
embodiment. MS 700 may comprise one or more features of MS 100
shown in FIG. 1, for example. In certain embodiments, processes
such as 200, 500, or 600, for example, may be implemented using
elements included in MS 700. In other embodiments, MS 700 may
provide a means for obtaining measurements of at least one
characteristic of one or more wireless signals acquired at the
mobile station while located in a signal environment; means for
obtaining a classification of a context of a user co-located with
the mobile station; and means for affecting application of a
representation of the signal environment to the measurements for
obtaining a position fix based, at least in part, on the
classification of the context. In still other embodiments, MS 700
may provide a means for obtaining measurements of at least one
characteristic of one or more wireless signals acquired at the
mobile station; means for determining a representation of a signal
environment in which the wireless signals were acquired based, at
least in part, on a detected context of the mobile station; and
means for estimating a location of the mobile station based, at
least in part, on a match of the obtained measurements with the
determined representation. For example, one or more of the means
recited above may be implemented by one or more of elements 711,
712, 721, 740, and/or 766, which will now be described in greater
detail. For example, MS 700 may comprise a wireless transceiver 721
which is capable of transmitting and receiving wireless signals 723
via an antenna 722 over a wireless communication network, such as
over a wireless communication link 123, shown in FIG. 1, for
example. Wireless transceiver 721 may be connected to bus 701 by a
wireless transceiver bus interface 720. Wireless transceiver bus
interface 720 may, in some embodiments be at least partially
integrated with wireless transceiver 721. Some embodiments may
include multiple wireless transceivers 721 and wireless antennas
722 to enable transmitting and/or receiving signals according to a
corresponding multiple wireless communication standards such as,
for example, WiFi, CDMA, WCDMA, LTE and Bluetooth, just to name a
few examples.
[0068] MS 700 may also comprise SPS receiver 755 capable of
receiving and acquiring SPS signals 759 via SPS antenna 758. SPS
receiver 755 may also process, in whole or in part, acquired SPS
signals 759 for estimating a location of MS 1000. In some
embodiments, general-purpose processor(s) 711, memory 740, DSP(s)
712 and/or specialized processors (not shown) may also be utilized
to process acquired SPS signals, in whole or in part, and/or
calculate an estimated location of MS 700, in conjunction with SPS
receiver 755. Storage of SPS or other signals for use in performing
positioning operations may be performed in memory 740 or registers
(not shown).
[0069] Also shown in FIG. 7, MS 700 may comprise digital signal
processor(s) (DSP(s)) 712 connected to the bus 701 by a bus
interface 710, general-purpose processor(s) 711 connected to the
bus 701 by a bus interface 710 and memory 740. Bus interface 710
may be integrated with the DSP(s) 712, general-purpose processor(s)
711 and memory 740. In various embodiments, functions or processes,
such as processes 200, 500, and 600 shown in FIGS. 2, 5, and 6, for
example, may be performed in response to execution of one or more
machine-readable instructions stored in memory 740 such as on a
computer-readable storage medium, such as RAM, ROM, FLASH, or disc
drive, just to name a few example. The one or more instructions may
be executable by general-purpose processor(s) 711, specialized
processors, or DSP(s) 712.
[0070] In one implementation, for example, one or more
machine-readable instructions stored in memory 740 may be
executable by a processor(s) 711 to perform processes such as
process 200, 500, or 600. In another implementation, for example,
one or more machine-readable instructions stored in memory 740 may
be executable by a processor(s) 711 to: obtain measurements of at
least one characteristic of one or more wireless signals acquired
at a mobile station while located in a signal environment; obtain a
classification of a context of a user co-located with the mobile
station; and affect application of a representation of the signal
environment to the measurements for obtaining a position fix based,
at least in part, on the classification of the context. In another
implementation, for example, one or more machine-readable
instructions stored in memory 740 may be executable by a
processor(s) 711 to: obtain measurements of at least one
characteristic of one or more wireless signals acquired at a mobile
station; determine a representation of a signal environment in
which the one or more wireless signals were acquired based, at
least in part, on a detected context of the mobile station; and
estimate a location of the mobile station based, at least in part,
on a match of the obtained measurements with the determined
representation. Memory 740 may comprise a non-transitory
processor-readable memory and/or a computer-readable memory that
stores software code (programming code, instructions, etc.) that
are executable by processor(s) 711 and/or DSP(s) 712 to perform
functions described herein.
[0071] Also shown in FIG. 7, a user interface 735 may comprise any
one of several devices such as, for example, a speaker, microphone,
display device, vibration device, keyboard, touch screen, just to
name a few examples. In a particular implementation, user interface
735 may enable a user to interact with one or more applications
hosted on MS 700. For example, devices of user interface 735 may
store analog or digital signals on memory 740 to be further
processed by DSP(s) 712 or general purpose processor 711 in
response to action from a user. Similarly, applications hosted on
MS 700 may store analog or digital signals on memory 740 to present
an output signal to a user. In another implementation, MS 700 may
optionally include a dedicated audio input/output (I/O) device 770
comprising, for example, a dedicated speaker, microphone, digital
to analog circuitry, analog to digital circuitry, amplifiers and/or
gain control. It should be understood, however, that this is merely
an example of how an audio I/O may be implemented in an MS, and
that claimed subject matter is not limited in this respect. In
another implementation, MS 700 may comprise touch sensors 762
responsive to touching or pressure on a keyboard or touch screen
device.
[0072] MS 700 may also comprise a dedicated camera device 764 for
capturing still or moving imagery. Camera device 764 may be used as
an environmental sensor, for example. Camera device 764 may
comprise, for example an imaging sensor (e.g., charge coupled
device or CMOS imager), lens, analog to digital circuitry, frame
buffers, just to name a few examples. In one implementation,
additional processing, conditioning, encoding or compression of
signals representing captured images may be performed at general
purpose/application processor 711 or DSP(s) 712. Alternatively, a
dedicated video processor 768 may perform conditioning, encoding,
compression or manipulation of signals representing captured
images. Additionally, video processor 768 may decode/decompress
stored image data for presentation on a display device 781 on MS
700.
[0073] MS 700 may also comprise sensors 760 coupled to bus 701
which may include, for example, inertial sensors and environment
sensors that may be used for ground-truth measurements, as
described above. Inertial sensors of sensors 760 may comprise, for
example accelerometers (e.g., collectively responding to
acceleration of MS 700 in three dimensions), one or more gyroscopes
or one or more magnetometers (e.g., to support one or more compass
applications). Environment sensors of MS 700 may comprise, for
example, temperature sensors, barometric pressure sensors, ambient
light sensors, camera imagers, and microphones, just to name few
examples. Sensors 760 may generate analog or digital signals that
may be stored in memory 740 and processed by DPS(s) or general
purpose processor 711 in support of one or more applications such
as, for example, applications directed to positioning or navigation
operations.
[0074] In a particular implementation, MS 700 may comprise a
dedicated modem processor 766 capable of performing baseband
processing of signals received and downconverted at wireless
transceiver 721 or SPS receiver 755. Similarly, modem processor 766
may perform baseband processing of signals to be upconverted for
transmission by wireless transceiver 721. In alternative
implementations, instead of having a dedicated modem processor,
baseband processing may be performed by a general purpose processor
or DSP (e.g., general purpose/application processor 711 or DSP(s)
712). It should be understood, however, that these are merely
examples of structures that may perform baseband processing, and
that claimed subject matter is not limited in this respect.
[0075] FIG. 8 is a schematic diagram illustrating an example system
800 that may include one or more devices configurable to implement
techniques or processes, such as process 800 described above, for
example, in connection with FIG. 7. System 800 may include, for
example, a first device 802, a second device 804, and a third
device 806, which may be operatively coupled together through a
wireless communications network 808. In an aspect, first device 802
may comprise a server capable of providing positioning assistance
data such as, for example, a base station almanac. First device 802
may also comprise a server capable of providing an LCI to a
requesting MS based, at least in part, on a rough estimate of a
location of the requesting MS. First device 802 may also comprise a
server capable of providing indoor positioning assistance data
relevant to a location of an LCI specified in a request from an MS.
Second and third devices 804 and 806 may comprise MSs, in an
aspect. In one implementation, second device 804 may comprise
elements that may be included in a server such as 140, 150, and/or
155. Also, in an aspect, wireless communications network 808 may
comprise one or more wireless access points, for example. However,
claimed subject matter is not limited in scope in these
respects.
[0076] First device 802, second device 804 and third device 806, as
shown in FIG. 8, may be representative of any device, appliance or
machine that may be configurable to exchange data over wireless
communications network 808. By way of example but not limitation,
any of first device 802, second device 804, or third device 806 may
include: one or more computing devices or platforms, such as, e.g.,
a desktop computer, a laptop computer, a workstation, a server
device, or the like; one or more personal computing or
communication devices or appliances, such as, e.g., a personal
digital assistant, mobile communication device, or the like; a
computing system or associated service provider capability, such
as, e.g., a database or data storage service provider/system, a
network service provider/system, an Internet or intranet service
provider/system, a portal or search engine service provider/system,
a wireless communication service provider/system; or any
combination thereof. Any of the first, second, and third devices
802, 804, and 806, respectively, may comprise one or more of a base
station almanac server, a base station, or an MS in accordance with
the examples described herein.
[0077] Similarly, wireless communications network 808, as shown in
FIG. 8, is representative of one or more communication links,
processes, or resources configurable to support the exchange of
data between at least two of first device 802, second device 804,
and third device 806. By way of example but not limitation,
wireless communications network 808 may include wireless or wired
communication links, telephone or telecommunications systems, data
buses or channels, optical fibers, terrestrial or space vehicle
resources, local area networks, wide area networks, intranets, the
Internet, routers or switches, and the like, or any combination
thereof. As illustrated, for example, by the dashed lined box
illustrated as being partially obscured of third device 806, there
may be additional like devices operatively coupled to wireless
communications network 808.
[0078] It is recognized that all or part of the various devices and
networks shown in system 800, and the processes and methods as
further described herein, may be implemented using or otherwise
including hardware, firmware, software, or any combination
thereof.
[0079] Thus, by way of example but not limitation, second device
804 may include at least one processing unit 820 that is
operatively coupled to a memory 822 through a bus 828. In one
implementation, for example, one or more machine-readable
instructions stored in memory 822 may be executable by processing
unit 820 to: receive a conceptual map of a navigable area, wherein
the conceptual map may include two or more topological elements
being related to one another in the conceptual map by a first set
of dimensions; apply one or more ground truth measurements or
topological constraints to the first set of dimensions of the
conceptual map to provide a modified map having corrected
dimensions; and map an estimated location of an MS to the modified
map.
[0080] Processing unit 820 is representative of one or more
circuits configurable to perform at least a portion of a data
computing procedure or process. By way of example but not
limitation, processing unit 820 may include one or more processors,
controllers, microprocessors, microcontrollers, application
specific integrated circuits, digital signal processors,
programmable logic devices, field programmable gate arrays, and the
like, or any combination thereof. In certain embodiments, processes
such as 200, 500, or 600, for example, may be performed by
processing unit 820. In other embodiments, input/output 832 may
provide a means for obtaining measurements of at least one
characteristic of one or more wireless signals acquired at a mobile
station while located in a signal environment. Processing unit 820
may provide a means for obtaining a classification of a context of
a user co-located with the mobile station and means for affecting
application of a representation of the signal environment to the
measurements for obtaining a position fix based, at least in part,
on the classification of the context. In still other embodiments,
input/output 832 may provide a means for obtaining measurements of
at least one characteristic of one or more wireless signals
acquired at a mobile station. Processing unit 820 may provide means
for determining a representation of a signal environment in which
the wireless signals were acquired based, at least in part, on a
detected context of the mobile station, and means for estimating a
location of the mobile station based, at least in part, on a match
of the obtained measurements with the determined
representation.
[0081] Memory 822 is representative of any data storage mechanism.
Memory 822 may include, for example, a primary memory 824 or a
secondary memory 826. Primary memory 824 may include, for example,
a random access memory, read only memory, etc. While illustrated in
this example as being separate from processing unit 820, it should
be understood that all or part of primary memory 824 may be
provided within or otherwise co-located/coupled with processing
unit 820.
[0082] Secondary memory 826 may include, for example, the same or
similar type of memory as primary memory or one or more data
storage devices or systems, such as, for example, a disk drive, an
optical disc drive, a tape drive, a solid state memory drive, etc.
In certain implementations, secondary memory 826 may be operatively
receptive of, or otherwise configurable to couple to, a
computer-readable medium 840. Computer-readable medium 840 may
include, for example, any non-transitory medium that can carry or
make accessible data, code or instructions for one or more of the
devices in system 800. Computer-readable medium 840 may also be
referred to as a storage medium.
[0083] Second device 804 may include, for example, a communication
interface 830 that provides for or otherwise supports the operative
coupling of second device 804 to at least wireless communications
network 808. By way of example but not limitation, communication
interface 830 may include a network interface device or card, a
modem, a router, a switch, a transceiver, and the like.
[0084] Second device 804 may include, for example, an input/output
device 832. Input/output device 832 is representative of one or
more devices or features that may be configurable to accept or
otherwise introduce human or machine inputs, or one or more devices
or features that may be configurable to deliver or otherwise
provide for human or machine outputs. By way of example but not
limitation, input/output device 832 may include an operatively
configured display, speaker, keyboard, mouse, trackball, touch
screen, data port, etc.
[0085] The methodologies described herein may be implemented by
various means depending upon applications according to particular
examples. For example, such methodologies may be implemented in
hardware, firmware, software, or combinations thereof. In a
hardware implementation, for example, a processing unit may be
implemented within one or more application specific integrated
circuits ("ASICs"), digital signal processors ("DSPs"), digital
signal processing devices ("DSPDs"), programmable logic devices
("PLDs"), field programmable gate arrays ("FPGAs"), processors,
controllers, micro-controllers, microprocessors, electronic
devices, other devices units designed to perform the functions
described herein, or combinations thereof.
[0086] Some portions of the detailed description included herein
are presented in terms of algorithms or symbolic representations of
operations on binary digital signals stored within a memory of a
specific apparatus or special purpose computing device or platform.
In the context of this particular specification, the term specific
apparatus or the like includes a general purpose computer once it
is programmed to perform particular operations pursuant to
instructions from program software. Algorithmic descriptions or
symbolic representations are examples of techniques used by those
of ordinary skill in the signal processing or related arts to
convey the substance of their work to others skilled in the art. An
algorithm is here, and generally, is considered to be a
self-consistent sequence of operations or similar signal processing
leading to a desired result. In this context, operations or
processing involve physical manipulation of physical quantities.
Typically, although not necessarily, such quantities may take the
form of electrical or magnetic signals capable of being stored,
transferred, combined, compared or otherwise manipulated. It has
proven convenient at times, principally for reasons of common
usage, to refer to such signals as bits, data, values, elements,
symbols, characters, terms, numbers, numerals, or the like. It
should be understood, however, that all of these or similar terms
are to be associated with appropriate physical quantities and are
merely convenient labels. Unless specifically stated otherwise, as
apparent from the discussion herein, it is appreciated that
throughout this specification discussions utilizing terms such as
"processing," "computing," "calculating," "determining" or the like
refer to actions or processes of a specific apparatus, such as a
special purpose computer, special purpose computing apparatus or a
similar special purpose electronic computing device. In the context
of this specification, therefore, a special purpose computer or a
similar special purpose electronic computing device is capable of
manipulating or transforming signals, typically represented as
physical electronic or magnetic quantities within memories,
registers, or other information storage devices, transmission
devices, or display devices of the special purpose computer or
similar special purpose electronic computing device.
[0087] Wireless communication techniques described herein may be in
connection with various wireless communications networks such as a
wireless wide area network ("WWAN"), a wireless local area network
("WLAN"), a wireless personal area network (WPAN), and so on. The
term "network" and "system" may be used interchangeably herein. A
WWAN may be a Code Division Multiple Access ("CDMA") network, a
Time Division Multiple Access ("TDMA") network, a Frequency
Division Multiple Access ("FDMA") network, an Orthogonal Frequency
Division Multiple Access ("OFDMA") network, a Single-Carrier
Frequency Division Multiple Access ("SC-FDMA") network, or any
combination of the above networks, and so on. A CDMA network may
implement one or more radio access technologies ("RATs") such as
cdma2000, Wideband-CDMA ("W-CDMA"), to name just a few radio
technologies. Here, cdma2000 may include technologies implemented
according to IS-95, IS-2000, and IS-856 standards. A TDMA network
may implement Global System for Mobile Communications ("GSM"),
Digital Advanced Mobile Phone System ("D-AMPS"), or some other RAT.
GSM and W-CDMA are described in documents from a consortium named
"3rd Generation Partnership Project" ("3GPP"). Cdma2000 is
described in documents from a consortium named "3rd Generation
Partnership Project 2" ("3GPP2"). 3GPP and 3GPP2 documents are
publicly available. 4G Long Term Evolution ("LTE") communications
networks may also be implemented in accordance with claimed subject
matter, in an aspect. A WLAN may comprise an IEEE 802.11x network,
and a WPAN may comprise a Bluetooth network, an IEEE 802.15x, for
example. Wireless communication implementations described herein
may also be used in connection with any combination of WWAN, WLAN
or WPAN.
[0088] In another aspect, as previously mentioned, a wireless
transmitter or access point may comprise a femto cell, utilized to
extend cellular telephone service into a business or home. In such
an implementation, one or more MSs may communicate with a femto
cell via a code division multiple access ("CDMA") cellular
communication protocol, for example, and the femto cell may provide
the MS access to a larger cellular telecommunication network by way
of another broadband network such as the Internet.
[0089] Techniques described herein may be used with an SPS that
includes any one of several GNSS and/or combinations of GNSS.
Furthermore, such techniques may be used with positioning systems
that utilize terrestrial transmitters acting as "pseudolites", or a
combination of SVs and such terrestrial transmitters. Terrestrial
transmitters may, for example, include ground-based transmitters
that broadcast a PN code or other ranging code (e.g., similar to a
GPS or CDMA cellular signal). Such a transmitter may be assigned a
unique PN code so as to permit identification by a remote receiver.
Terrestrial transmitters may be useful, for example, to augment an
SPS in situations where SPS signals from an orbiting SV might be
unavailable, such as in tunnels, mines, buildings, urban canyons or
other enclosed areas. Another implementation of pseudolites is
known as radio-beacons. The term "SV", as used herein, is intended
to include terrestrial transmitters acting as pseudolites,
equivalents of pseudolites, and possibly others. The terms "SPS
signals" and/or "SV signals", as used herein, is intended to
include SPS-like signals from terrestrial transmitters, including
terrestrial transmitters acting as pseudolites or equivalents of
pseudolites.
[0090] The terms, "and," and "or" as used herein may include a
variety of meanings that will depend at least in part upon the
context in which it is used. Typically, "or" if used to associate a
list, such as A, B or C, is intended to mean A, B, and C, here used
in the inclusive sense, as well as A, B or C, here used in the
exclusive sense. Reference throughout this specification to "one
example" or "an example" means that a particular feature,
structure, or characteristic described in connection with the
example is included in at least one example of claimed subject
matter. Thus, the appearances of the phrase "in one example" or "an
example" in various places throughout this specification are not
necessarily all referring to the same example. Furthermore, the
particular features, structures, or characteristics may be combined
in one or more examples. Examples described herein may include
machines, devices, engines, or apparatuses that operate using
digital signals. Such signals may comprise electronic signals,
optical signals, electromagnetic signals, or any form of energy
that provides information between locations.
[0091] While there has been illustrated and described what are
presently considered to be example features, it will be understood
by those skilled in the art that various other modifications may be
made, and equivalents may be substituted, without departing from
claimed subject matter. Additionally, many modifications may be
made to adapt a particular situation to the teachings of claimed
subject matter without departing from the central concept described
herein. Therefore, it is intended that claimed subject matter not
be limited to the particular examples disclosed, but that such
claimed subject matter may also include all aspects falling within
the scope of appended claims, and equivalents thereof.
* * * * *